{"id":"https://openalex.org/W2973103872","doi":"https://doi.org/10.1145/3347146.3359348","title":"Road Network Reconstruction from satellite images with Machine Learning Supported by Topological Methods","display_name":"Road Network Reconstruction from satellite images with Machine Learning Supported by Topological Methods","publication_year":2019,"publication_date":"2019-11-05","ids":{"openalex":"https://openalex.org/W2973103872","doi":"https://doi.org/10.1145/3347146.3359348","mag":"2973103872"},"language":"en","primary_location":{"id":"doi:10.1145/3347146.3359348","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3347146.3359348","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359348","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359348","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025354167","display_name":"Tamal K. Dey","orcid":"https://orcid.org/0000-0001-5160-9738"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tamal K. Dey","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101696741","display_name":"Jiayuan Wang","orcid":"https://orcid.org/0000-0003-2354-8696"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayuan Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101536052","display_name":"Yusu Wang","orcid":"https://orcid.org/0000-0001-7950-4348"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yusu Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5025354167"],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":2.8579,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.89848077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"520","last_page":"523"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9729999899864197,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7840607762336731},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7540581822395325},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6179364919662476},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5534852743148804},{"id":"https://openalex.org/keywords/morse-code","display_name":"Morse code","score":0.4862869679927826},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42638784646987915},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41205596923828125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4106065034866333},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3928194046020508},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3245428800582886},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23548954725265503}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7840607762336731},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7540581822395325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6179364919662476},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5534852743148804},{"id":"https://openalex.org/C140031139","wikidata":"https://www.wikidata.org/wiki/Q79897","display_name":"Morse code","level":2,"score":0.4862869679927826},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42638784646987915},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41205596923828125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4106065034866333},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3928194046020508},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3245428800582886},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23548954725265503},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3347146.3359348","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3347146.3359348","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359348","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1909.06728","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.06728","pdf_url":"https://arxiv.org/pdf/1909.06728","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3347146.3359348","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3347146.3359348","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359348","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5400000214576721}],"awards":[{"id":"https://openalex.org/G1302751805","display_name":"RI: Small: Learning discrete structure from continuous spaces","funder_award_id":"1815697","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3187039853","display_name":null,"funder_award_id":"RI-1815697","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3778806257","display_name":"AF: Small: Collaborative Research:Geometric and topological algorithms for analyzing road network data","funder_award_id":"1618247","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5696833129","display_name":null,"funder_award_id":"CCF-1740761, RI-1815697, CCF-1733798, CCF-1618247","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6367296400","display_name":null,"funder_award_id":"1740761","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7622574371","display_name":"AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures","funder_award_id":"1733798","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2973103872.pdf","grobid_xml":"https://content.openalex.org/works/W2973103872.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1983948426","https://openalex.org/W2064020260","https://openalex.org/W2096736341","https://openalex.org/W2097375363","https://openalex.org/W2134430772","https://openalex.org/W2143972956","https://openalex.org/W2143983457","https://openalex.org/W2146019744","https://openalex.org/W2150049246","https://openalex.org/W2150372924","https://openalex.org/W2167510172","https://openalex.org/W2194775991","https://openalex.org/W2292658783","https://openalex.org/W2300131316","https://openalex.org/W2560023338","https://openalex.org/W2779134112","https://openalex.org/W2790334719","https://openalex.org/W2811199523","https://openalex.org/W2913014488","https://openalex.org/W2962978395","https://openalex.org/W2963376197","https://openalex.org/W2973103872","https://openalex.org/W3013843370","https://openalex.org/W3099367142","https://openalex.org/W3101384751","https://openalex.org/W3140579943"],"related_works":["https://openalex.org/W4224264613","https://openalex.org/W652105763","https://openalex.org/W4251604918","https://openalex.org/W4252272066","https://openalex.org/W4298428686","https://openalex.org/W2027332925","https://openalex.org/W2130611154","https://openalex.org/W2808402399","https://openalex.org/W2808170731","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Automatic":[0],"Extraction":[1],"of":[2,135,149,186,200],"road":[3],"network":[4],"from":[5,146,164,195],"satellite":[6,110],"images":[7,150,167],"is":[8],"a":[9,67,71,91,96,127,144,147],"goal":[10],"that":[11,20,114],"can":[12,50,57],"benefit":[13],"and":[14,25,122,154],"even":[15],"enable":[16],"new":[17],"technologies.":[18],"Methods":[19],"combine":[21,95],"machine":[22],"learning":[23],"(ML)":[24],"computer":[26],"vision":[27],"have":[28],"been":[29],"proposed":[30],"in":[31,86,90,126,204],"recent":[32],"years":[33],"which":[34],"make":[35],"the":[36,41,133,136,156,161,165,170,175,184,187,201],"task":[37],"semi-automatic":[38,92],"by":[39,69,169],"requiring":[40],"user":[42],"to":[43,107,117,142,159,182],"provide":[44],"curated":[45],"training":[46,55],"samples.":[47],"The":[48],"process":[49],"be":[51,58],"fully":[52,128],"automatized":[53],"if":[54],"samples":[56],"produced":[59],"algorithmically.":[60],"In":[61],"this":[62,115],"work,":[63],"we":[64,94,131],"develop":[65],"such":[66],"technique":[68],"infusing":[70],"persistence-guided":[72],"discrete":[73],"Morse":[74],"based":[75,98,138,177],"graph":[76,99,139,178],"reconstruction":[77,100,140,179],"algorithm":[78,101,141,158,180],"into":[79],"ML":[80],"framework.":[81],"We":[82,112,173,189],"elucidate":[83],"our":[84],"contributions":[85],"two":[87],"phases.":[88],"First,":[89],"framework,":[93,130],"discrete-Morse":[97,137,176],"with":[102,119],"an":[103],"existing":[104],"CNN":[105,145],"framework":[106],"segment":[108],"input":[109],"images.":[111],"show":[113,190],"leads":[116],"reconstructions":[118],"better":[120],"connectivity":[121],"less":[123],"noise.":[124],"Next,":[125],"automatic":[129],"leverage":[132],"power":[134],"train":[143],"collection":[148],"without":[151],"labelled":[152],"data":[153],"use":[155],"same":[157],"produce":[160],"final":[162],"output":[163],"segmented":[166],"created":[168],"trained":[171],"CNN.":[172,188],"apply":[174],"iteratively":[181],"improve":[183],"accuracy":[185],"experimental":[191],"results":[192],"on":[193],"datasets":[194],"SpaceNet":[196],"Challenge.":[197],"Full":[198],"version":[199],"paper":[202],"appears":[203],"[8].":[205]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
